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We propose using one-class, two-class, and multiclass SVMs to annotate images for supporting keyword retrieval of images. Providing automatic annotation requires an accurate mapping of images' low-level perceptual features (e.g., color and texture) to some high-level semantic labels (e.g., landscape, architecture, and animals). Much work has been performed in this area; however, there is a lack of ability to assess the quality of annotation. In this paper, we propose a confidence-based dynamic ensemble (CDE), which employs a three-level classification scheme. At the base-level, CDE uses one-class Support Vector Machines (SVMs) to characterize a confidence factor for ascertaining the correctness of an annotation (or a class prediction) made by a binary SVM classifier. The confidence factor is then propagated to the multiclass classifiers at subsequent levels. CDE uses the confidence factor to make dynamic adjustments to its member classifiers so as to improve class-prediction accuracy, to accommodate new semantics, and to assist in the discovery of useful low-level features. Our empirical studies on a large real-world data set demonstrate CDE to be very effective.
Index Terms- Pattern recognition, models, statistical, artificial intelligence, learning.

K. Goh, B. Li and E. Y. Chang, "Using One-Class and Two-Class SVMs for Multiclass Image Annotation," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 1333-1346, 2005.
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